FAED: fuzzy attention-aided encoder-decoder architecture for skin lesion segmentation

Accurate skin lesion segmentation is essential for early melanoma diagnosis, but remains challenging due to low contrast and ambiguous lesion boundaries. Current deep learning methods often employ standard attention mechanisms with binary gating, which may not fully capture the gradual transitions between lesion and healthy skin. To this end, we propose Fuzzy Attention-aided Encoder-Decoder (FAED) for improved skin lesion segmentation. This architecture integrates soft fuzzy attention mechanism into a U-Net backbone, replacing standard skip connections with a Boundary-conditioned Soft Fuzzy Attention (BSFA) module. BSFA utilizes learnable Gaussian membership functions to model feature relevance probabilistically, rather than using binary selection. Additionally, we introduce an Adaptive Fuzzy Mixture-based aggregation to handle feature variations. Additionally, a Boundary Cue is employed to explicitly refine the attention focus at the lesion perimeters. In addition, empirical profiling on an NVIDIA Tesla T4 shows that FAED performs inference in 10.05 ms/image at batch size 1 and 6.76 ms/image at batch size 8, while requiring 505 MB and 948 MB peak GPU memory during inference, respectively. The proposed architecture maintains low computational cost (2.4M parameters, \(~ \) 4 GFLOPs) while improving segmentation performance. Evaluations on ISIC2018, ISIC2017, ISIC2016, and PH \(^2\) datasets demonstrate that FAED achieves competitive performance, yielding Dice scores of 0.9140 on ISIC2016, 0.9135 on PH \(^2\) , 0.8781 on ISIC2018, and 0.8615 on ISIC17. Ablation studies confirm that both prototype-based aggregation and boundary-conditioned modulation contribute to the observed improvements. These results suggest that modeling feature fusion as soft similarity-based membership, combined with explicit boundary guidance, can improve skin lesion segmentation without increasing architectural complexity.